To demonstrate the versatility of the proposed method, we also experiment with outdoor data.
For this purpose, we train and test our model with the KITTI depth completion dataset [1]}, in which we perform on par with state-of-the-art methods using a significantly smaller number of parameters as shown in Table REF . We notice that there is a clear trade-off between model parameters and performance as shown in Figure REF  b). Figure REF  presents qualitative comparison with baseline methods. The proposed method produces finer depth details as emphasized in the highlight areas. However, the difference is largest with a small number of input 3D points as depicted in Figure REF  d). The results suggest that high-quality depth maps can be obtained by using only a few LiDAR points enabling more cost efficient solutions. Additional results are also added to the supplementary material.
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